AI is changing the software development lifecycle by making the most visible part feel smaller.
Coding time can shrink dramatically. That is useful, but it also distorts attention. If the team reads generated code as the center of the work, it will underinvest in the parts AI has made more important: framing, boundaries, constraints, integration, security, and review.
Specs become the quality lever.
That can sound like a return to waterfall, mostly because the industry has spent years treating upfront thought as suspicious. But heavy specification is not automatically nostalgia. Sometimes it is respect for unknowns. A model that can produce a lot of code quickly needs clearer intent than a human team that can negotiate ambiguity over weeks of implementation.
Brownfield systems make this even less optional. Legacy modernization still needs sequencing. Strangler-style paths still matter. Dependencies, data contracts, operational risk, and user disruption do not become simpler because an agent can generate a candidate implementation.
The durable skill is problem decomposition. Someone has to decide what should exist, what must never happen, where risk hides, and how the system will prove it is safe enough to trust.
Syntax got cheaper. Judgment did not.
The work did not disappear. It moved to the part of delivery that was easiest to skip when coding still felt like progress.
Related episode: AI Is Changing the SDLC: Coding Matters Less, Specs Matter More.
